
R version 4.3.1 (2023-06-16) -- "Beagle Scouts"
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Platform: aarch64-apple-darwin20 (64-bit)

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[Previously saved workspace restored]

> # create a predicting matrix with all the independent variables and save the matrix
> 
> rm(list=ls())
> 
> # load similarity matrix(s)
> state_similarity_matrix <- readRDS("processed_data/state_similarity_matrix.Rds")
> party_similarity_matrix <- readRDS("processed_data/party_similarity_matrix.Rds")
> chamber_similarity_matrix <- readRDS("processed_data/chamber_similarity_matrix.Rds")
> gender_similarity_matrix <- readRDS("processed_data/gender_similarity_matrix.Rds")
> race_similarity_matrix <- readRDS("processed_data/same_race.Rds")
> profesh_diff_matrix <- readRDS("processed_data/profeshScore_diff.Rds")
> 
> # Sender matrix
> democrat_xs <- readRDS("processed_data/democrat_sender_effect.Rds")
> republican_xs <- readRDS("processed_data/republican_sender_effect.Rds")
> house_xs <- readRDS("processed_data/house_sender_matrix.Rds")
> gender_xs <- readRDS("processed_data/gender_sender_matrix.Rds")
> profesh_xs <- readRDS("processed_data/sender_profesh.Rds")
> black_xs <- readRDS("processed_data/sender_black.Rds")
> latino_xs <- readRDS("processed_data/sender_latino.Rds")
> asian_xs <- readRDS("processed_data/sender_asian.Rds")
> mena_xs <- readRDS("processed_data/sender_mena.Rds")
> multi_xs <- readRDS("processed_data/sender_multi.Rds")
> native_xs <- readRDS("processed_data/sender_native.Rds")
> 
> # Receiver effects
> democrat_xr <- readRDS("processed_data/democrat_receiver_effect.Rds")
> republican_xr <- readRDS("processed_data/republican_receiver_effect.Rds")
> house_xr <- readRDS("processed_data/house_receiver_matrix.Rds")
> gender_xr <- readRDS("processed_data/gender_receiver_matrix.Rds")
> profesh_xr <- readRDS("processed_data/receiver_profesh.Rds")
> black_xr <- readRDS("processed_data/receiver_black.Rds")
> latino_xr <- readRDS("processed_data/receiver_latino.Rds")
> asian_xr <- readRDS("processed_data/receiver_asian.Rds")
> mena_xr <- readRDS("processed_data/receiver_mena.Rds")
> multi_xr <- readRDS("processed_data/receiver_multi.Rds")
> native_xr <- readRDS("processed_data/receiver_native.Rds")
> 
> ## Diff state matrix 
> #diff_state_matrix <- state_similarity_matrix - 1 
> 
> # Interaction effect with diff state 
> party_DiffState_interaction_matrix <- readRDS("processed_data/party_DiffState_interaction_matrix.Rds")
> chamber_DiffState_interaction_matrix <- readRDS("processed_data/chamber_DiffState_interaction_matrix.Rds")
> gender_DiffState_interaction_matrix <- readRDS("processed_data/gender_DiffState_interaction_matrix.Rds")
> race_DiffState_interaction_matrix <- readRDS("processed_data/race_DiffState_interaction_matrix.Rds")
> 
> 
> ## Interaction effects with sme state 
> #party_DiffState_interaction_matrix <- readRDS("processed_data/party_sameState_interaction_matrix.Rds")
> #chamber_sameState_interaction_matrix <- readRDS("processed_data/chamber_sameState_interaction_matrix.Rds")
> #gender_sameState_interaction_matrix <- readRDS("processed_data/gender_sameState_interaction_matrix.Rds")
> #race_sameState_interaction_matrix <- readRDS("processed_data/race_sameState_interaction_matrix.Rds")
> 
> # Contiguos  states
> contiguous <- readRDS("processed_data/contig_states_matrix.Rds")
> 
> # join the predicting matrices together
> predicting_matrices <- array (NA, c(33, 
+                                     length(state_similarity_matrix[1,]), 
+                                     length(state_similarity_matrix[1,])))
> 
> # Similarity
> predicting_matrices[1,,] <- state_similarity_matrix
> rm(state_similarity_matrix)
> 
> predicting_matrices[2,,] <- party_similarity_matrix
> rm(party_similarity_matrix)
> 
> predicting_matrices[3,,] <- chamber_similarity_matrix
> rm(chamber_similarity_matrix)
> 
> predicting_matrices[4,,] <- gender_similarity_matrix
> rm(gender_similarity_matrix)
> 
> predicting_matrices[5,,] <- race_similarity_matrix
> rm(race_similarity_matrix)
> 
> predicting_matrices[6,,] <- profesh_diff_matrix
> rm(profesh_diff_matrix)
> 
> 
> # Sender Effects
> predicting_matrices[7,,] <- democrat_xs
> rm(democrat_xs)
> 
> predicting_matrices[8,,] <- republican_xs
> rm(republican_xs)
> 
> predicting_matrices[9,,] <- house_xs
> rm(house_xs)
> 
> predicting_matrices[10,,] <- gender_xs
> rm(gender_xs)
> 
> predicting_matrices[11,,] <- profesh_xs
> rm(profesh_xs)
> 
> predicting_matrices[12,,] <- black_xs
> rm(black_xs)
> 
> predicting_matrices[13,,] <- latino_xs
> rm(latino_xs)
> 
> predicting_matrices[14,,] <- asian_xs
> rm(asian_xs)
> 
> predicting_matrices[15,,] <- mena_xs
> rm(mena_xs)
> 
> predicting_matrices[16,,] <- multi_xs
> rm(multi_xs)
> 
> predicting_matrices[17,,] <- native_xs
> rm(native_xs)
> 
> 
> # Receiver Effects
> predicting_matrices[18,,] <- democrat_xr
> rm(democrat_xr)
> 
> predicting_matrices[19,,] <- republican_xr
> rm(republican_xr)
> 
> predicting_matrices[20,,] <- house_xr
> rm(house_xr)
> 
> predicting_matrices[21,,] <- gender_xr
> rm(gender_xr)
> 
> predicting_matrices[22,,] <- profesh_xr
> rm(profesh_xr)
> 
> predicting_matrices[23,,] <- black_xr
> rm(black_xr)
> 
> predicting_matrices[24,,] <- latino_xr
> rm(latino_xr)
> 
> predicting_matrices[25,,] <- asian_xr
> rm(asian_xr)
> 
> predicting_matrices[26,,] <- mena_xr
> rm(mena_xr)
> 
> predicting_matrices[27,,] <- multi_xr
> rm(multi_xr)
> 
> predicting_matrices[28,,] <- native_xr
> rm(native_xr)
> 
> # Interaction Effects
> predicting_matrices[29,,] <- party_DiffState_interaction_matrix
> rm(party_DiffState_interaction_matrix)
> 
> predicting_matrices[30,,] <- chamber_DiffState_interaction_matrix
> rm(chamber_DiffState_interaction_matrix)
> 
> predicting_matrices[31,,] <- gender_DiffState_interaction_matrix
> rm(gender_DiffState_interaction_matrix)
> 
> predicting_matrices[32,,] <- race_DiffState_interaction_matrix
> rm(race_DiffState_interaction_matrix)
> 
> # Contiguous States 
> predicting_matrices[33,,] <- contiguous
> rm(contiguous)
> 
> save(predicting_matrices, file = "processed_data/QAP_predicting_matrices_2.RData")
> 
> proc.time()
   user  system elapsed 
 44.650   2.401  48.054 
